We first study the impact of effective multi-view on the Nerfies-HyperNeRF [1]}, [2]} dataset.
In this experiment, we rectify the effective multi-view sequences by only using the left camera during training as opposed to both the left and right cameras, illustrated in Figure REF .
We denote the original setting as “teleporting” and the rectified sequences as “non-teleporting”.
We train all approaches under these two settings with the same held-out validation frames and same set of co-visibility masks computed from common training frames.
In Figure REF  (Top), all methods perform better across all metrics when trained under the teleporting setting compared to the non-teleporting one, with the exception of PCK-T for NSFF.
We conjecture that this is because that NSFF has additional optical flow supervision, which is more accurate without camera teleportation.
In Figure REF  (Bottom), we show qualitative results using Nerfies (we include visualizations of the other methods in the Appendix).
Without effective multi-view, Nerfies fails at modeling physically plausible shape for broom and wires.
Our results show that the effective multi-view in the existing experimental protocol inflates the synthesis quality of prior methods, and that truly monocular captures are more challenging.
